Intelligent System to Predict University Students Dropout

Abstract

The objective of this research is to reduce the dropout rate of students in the Faculty of Systems Engineering and Informatics of the Universidad Nacional Mayor de San Marcos – FISI-UNMSM, through the implementation of an intelligent system with a data mining approach and the autonomous learning algorithm (decision trees) that predicts which students are at risk of dropping out. It was developed in Python and the free software Weka, for this purpose student data was collected from 2014 to 2020. This solution increases the availability and the level of satisfaction of the faculty; in the learning process, an accuracy percentage of 90.34% and precision of 95.91% was obtained, so the data mining model is considered valid. In addition, it was found that the variables that most influenced students in making the decision to abandon their studies were the historical weighted average, the weighted average of the last cycle and the number of credits passed.

Post-pandemic psychosocial variables affecting academic dropout in a sample of Chilean higher-education students

“…In 2020, more than 300,000 university students worldwide dropped out of school. The university dropout rate reached 18.6% in 2020, an indicator six points higher than that recorded in 2019, equivalent to 12% (Vega et al, 2022). The dropout rate in some studies in the first and second year reaches 44.8, 15.9% in the third year, and 15.9% in the fourth year (Alban and Mauricio, 2019).…”

“…On the other hand, other factors relevant to a student's tendency to drop out are the historical weighted average of their grades, the weighted average of their grades in the last cycle, or the number of credits passed in their courses (Vega et al, 2022). The critical problem for many tertiary-level students is learning how to learn.…”

Artificial Intelligence (AI) in Education: Unlocking the Perfect Synergy for Learning

“…An example of this approach can be found in the work of Vega et al (2022), who designed an intelligent agent that adapts the teaching-learning process according to students' profiles, significantly improving their academic performance. This approach has the potential to improve the effectiveness of education and reduce university dropout rates by personalizing the learning experience of each student and adapting it to their individual needs.…”

Knowledge management using virtual environments to improve learning in software production

“…El 74% de los estudiantes perciben que el AV mejora el aprendizaje (Morales-Romero et al, 2022), estas mejoras no sólo permiten mejores resultados de los docentes sino que también ayudan a prevenir el abandono estudiantil (Vega et al, 2022), (De-La-Cruz et al, 2022).…”